## Code that creates Figure where we provide separate estimates for left and right party nominations.
## Last changed: 2025-05-23

sink("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Output/FigureA18.txt", split=TRUE)

library(PSweight)
library(cobalt)
library(MatchIt)
library(distances)
library(moreparty)
library(party)
library(caret)
library(bonsai)
library(randomForest)
library(tidymodels)
library(tidyverse)
library(rmarkdown)
library(tinytex)
library(tibble)
library(knitr)
library(car)
library(fst)
library(stargazer)
library(pryr)
library(stringr)
library(fst)
library(lubridate)
library(data.table)
library(summarytools)
library(ggplot2)
library(cowplot)
library(ggpubr)
library(haven)
library(broom)
library(fixest)
library(modelsummary)
library(gt)
library(MASS)
library(survey)
library(collapse)
library(readxl)
library(Hmisc)
library(DescTools)
library(ranger)
library(stablelearner)
library(future.apply)
library(lme4)
library(partykit)
library(ggiplot)


############################### Dependent variable: Nominated for left party ######################################
# Specify the name of the treatment variable
  tvar <- "T94_90"
  
# Specify the the birth cohorts to be used for the analysis
  lbyr <- 1942
  ubyr <- 1967
  
# Specify if only include Swedish citizens 
  citizen <- 1
  
# Specify the propensity score specification
  psformula <- formula(T94 ~
                         Kon + DAStod_1982 + DAStod_1983 + DAStod_1984 +
                         DAStod_1985 + DAStod_1986 + DAStod_1987 + DAStod_1988 +
                         DAStod_1989 +
                         DAntBarn_91 + Sambo_91 + InvBak  |
                         SSYK3_90 + SNI2_91 + Kommun_91 +
                         rCARB_1982 + rCARB_1983 + rCARB_1984 + rCARB_1985 +
                         rCARB_1986 + rCARB_1987 + rCARB_1988 + rCARB_1989 +
                         pArbInk_1990 + pArbInk_1991 + UtbAr_91 +
                         FodAr + aNom82_van + aNom85_van + aNom88_van + aNom91_van)

## Estimate two models, one for the complete sample including all observations,
## the second on the restricted sample only including individuals for which
## we can observe all elections bw 82-18 in which they are eligible to run for office. 

  Models <-  vector('list', 10)
  j <- 1

for(urval in c(1)) { 
 for(i in 1:5) { 
  # Read in the data sets
  NomVald <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_NomVald.fst", as.data.table=T)
  RTB <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_RTB.fst", as.data.table=T)
  db <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_Nom.fst", as.data.table=T)
  
  db[, T94 := get(tvar)]
  mdb <- db[between(FodAr, lbyr, ubyr) & (SvMedb82_18 >= citizen) & (AllaVal >= urval),]
  rm(db)
  gc()
  
  mdb[Nom82 == -99, aNom82_van := -99]
  
  # Split the in SES quartiles
  mdb[!is.na(avSES3), ravSES := cut_number(avSES3, 4, labels = FALSE)]
  if(i==1) mdb <- mdb[between(ravSES, 1, 1)]
  if(i==2) mdb <- mdb[between(ravSES, 2, 2)]
  if(i==3) mdb <- mdb[between(ravSES, 3, 3)]
  if(i==4) mdb <- mdb[between(ravSES, 4, 4)]
  
# Use logit model to estimate propensity scores
  mdb <- mdb[complete.cases(mdb),]
  tm <- proc.time()
  logmod <- feglm(psformula,  
                data = mdb, family="binomial")
  proc.time()-tm
  summary(logmod)

# Extract predicted probability (propensity scores)
  if(logmod$nobs == logmod$nobs_origin){ 
    mdb[, pT94 := predict(logmod, type = "response")]
  }else{
    mdb[logmod$obs_selection$obsRemoved, pT94 := predict(logmod, type = "response")]
  }

  mdb <- mdb[complete.cases(mdb),]

# Now use the Full matching approach of Sävje et al. to obtain ATT and ATE weights.  

  tmp <- proc.time()
  set.seed(7892)
  m.out <- matchit(T94~1, data = mdb,
                 distance = mdb$pT94, method = "quick", estimand = "ATT")
  proc.time()-tmp
  m.data1 <- as.data.table(match.data(m.out))[, .(LopNr, weights)]
  setnames(m.data1, "weights", "gm_att")

  mdb <- m.data1[, .(LopNr, gm_att)][mdb, on = "LopNr"]
  gc()
  RTB <- mdb[RTB, on = "LopNr"]

  rm(mdb)
  gc()

  rm(list=setdiff(ls(), c("NomVald", "RTB", "rtbvar", "Models", "j", "tvar", "lbyr", "ubyr", "citizen", "psformula", "urval")))
  gc()


  mdb <- RTB[!is.na(gm_att), 
             .(Kon, Nom82, Nom85, Nom88, Nom91, Sysselsatt_85, DAntBarn_91, Sambo_91, 
               DStud_91, DForLed_91, DSjukRe_91, DSocBidrFam_91, DBostBidrFam_91,
               UtbAr_91, FodAr, InvBak, isei, Yrke80_90, SNI2_90, Kommun_91, fp_LoneInk_85, fp_LoneInk_90, fp_LoneInk_91,
               gm_att, aNom_van, LopNr, T94, Ar, pT94)]

  gc()
  
  mdb[, aNom_van := aNom_van*100]
  mdb[, cID := .N, by=.(LopNr, Ar)]
  
  rm(RTB)
  gc()

  
  Models[[j]] <- feols(aNom_van~T94+i(Ar, T94, ref=1991) | Ar, cluster ="LopNr", 
                       weights = mdb[, gm_att], 
                       mdb[])
  
  
  print(summary(Models[[j]]))
  print(qsu(mdb[Ar==1991, aNom_van]))
  print(uniqueN(mdb[T94==0, LopNr]))
  print(uniqueN(mdb[T94==1, LopNr]))
  print(uniqueN(mdb[, LopNr]))
  print(dim(mdb))
  print(qsu(mdb$cID))
  j <- j+1
}  
}  

  ############################### Dependent variable: Nominated for right party ######################################
  
  # Specify the name of the treatment variable
  tvar <- "T94_90"
  
  # Specify the the birth cohorts to be used for the analysis
  lbyr <- 1942
  ubyr <- 1967
  
  # Specify if only include Swedish citizens 
  citizen <- 1
  
  # Specify the propensity score specification
  psformula <- formula(T94 ~
                         Kon + DAStod_1982 + DAStod_1983 + DAStod_1984 +
                         DAStod_1985 + DAStod_1986 + DAStod_1987 + DAStod_1988 +
                         DAStod_1989 +
                         DAntBarn_91 + Sambo_91 + InvBak  |
                         SSYK3_90 + SNI2_91 + Kommun_91 +
                         rCARB_1982 + rCARB_1983 + rCARB_1984 + rCARB_1985 +
                         rCARB_1986 + rCARB_1987 + rCARB_1988 + rCARB_1989 +
                         pArbInk_1990 + pArbInk_1991 + UtbAr_91 +
                         FodAr + aNom82_hog + aNom85_hog + aNom88_hog + aNom91_hog)
  
  ## Estimate two models, one for the complete sample including all observations,
  ## the second on the restricted sample only including individuals for which
  ## we can observe all elections bw 82-18 in which they are eligible to run for office. 
  
  for(urval in c(1)) { 
    for(i in 1:5) { 
      # Read in the data sets
      NomVald <- read_fst("E:/ProjData/Jan_Kalle/DB_NomVald.fst", as.data.table=T)
      RTB <- read_fst("E:/ProjData/Jan_Kalle/DB_RTB.fst", as.data.table=T)
      db <- read_fst("E:/ProjData/Jan_Kalle/DB_Nom.fst", as.data.table=T)
      
      db[, T94 := get(tvar)]
      mdb <- db[between(FodAr, lbyr, ubyr) & (SvMedb82_18 >= citizen) & (AllaVal >= urval),]
      rm(db)
      gc()
      
      mdb[Nom82 == -99, aNom82_hog := -99]
      
      # Split the in SES quartiles
      mdb[!is.na(avSES3), ravSES := cut_number(avSES3, 4, labels = FALSE)]
      if(i==1) mdb <- mdb[between(ravSES, 1, 1)]
      if(i==2) mdb <- mdb[between(ravSES, 2, 2)]
      if(i==3) mdb <- mdb[between(ravSES, 3, 3)]
      if(i==4) mdb <- mdb[between(ravSES, 4, 4)]
      
      # Use logit model to estimate propensity scores
      mdb <- mdb[complete.cases(mdb),]
      tm <- proc.time()
      logmod <- feglm(psformula,  
                      data = mdb, family="binomial")
      proc.time()-tm
      summary(logmod)
      
      # Extract predicted probability (propensity scores)
      if(logmod$nobs == logmod$nobs_origin){ 
        mdb[, pT94 := predict(logmod, type = "response")]
      }else{
        mdb[logmod$obs_selection$obsRemoved, pT94 := predict(logmod, type = "response")]
      }
      
      mdb <- mdb[complete.cases(mdb),]
      
      # Now use the Full matching approach of Sävje et al. to obtain ATT and ATE weights.  
      
      tmp <- proc.time()
      set.seed(7892)
      m.out <- matchit(T94~1, data = mdb,
                       distance = mdb$pT94, method = "quick", estimand = "ATT")
      proc.time()-tmp
      m.data1 <- as.data.table(match.data(m.out))[, .(LopNr, weights)]
      setnames(m.data1, "weights", "gm_att")
      
      mdb <- m.data1[, .(LopNr, gm_att)][mdb, on = "LopNr"]
      gc()
      RTB <- mdb[RTB, on = "LopNr"]
      
      rm(mdb)
      gc()
      
      rm(list=setdiff(ls(), c("NomVald", "RTB", "rtbvar", "Models", "j", "tvar", "lbyr", "ubyr", "citizen", "psformula", "urval")))
      gc()
      
      
      mdb <- RTB[!is.na(gm_att), 
                 .(Kon, Nom82, Nom85, Nom88, Nom91, Sysselsatt_85, DAntBarn_91, Sambo_91, 
                   DStud_91, DForLed_91, DSjukRe_91, DSocBidrFam_91, DBostBidrFam_91,
                   UtbAr_91, FodAr, InvBak, isei, Yrke80_90, SNI2_90, Kommun_91, fp_LoneInk_85, fp_LoneInk_90, fp_LoneInk_91,
                   gm_att, aNom_hog, LopNr, T94, Ar, pT94)]
      
      gc()
      
      mdb[, aNom_hog := aNom_hog*100]
      mdb[, cID := .N, by=.(LopNr, Ar)]
      
      rm(RTB)
      gc()
      
      
      Models[[j]] <- feols(aNom_hog~T94+i(Ar, T94, ref=1991) | Ar, cluster ="LopNr", 
                           weights = mdb[, gm_att], 
                           mdb[])
      
      
      print(summary(Models[[j]]))
      print(qsu(mdb[Ar==1991, aNom_hog]))
      print(uniqueN(mdb[T94==0, LopNr]))
      print(uniqueN(mdb[T94==1, LopNr]))
      print(uniqueN(mdb[, LopNr]))
      print(dim(mdb))
      print(qsu(mdb$cID))
      j <- j+1
    }  
  }  
  
  
  valar <- c(1982, 1985, 1988, 1991, 1994, 1998, 2002, 2006, 2010, 2014, 2018, 2022)
  options(scipen=999)
  
    for(i in 1:10) {
      
      ses <- ""
      if(i %in% c(1, 2, 3, 4)) ses <- paste0("Leftist Parties ",  "SES Q", i)
      if(i %in% c(5)) ses <- paste0("Leftist Parties ",  "All SES")
      if(i %in% c(6, 7, 8, 9)) ses <- paste0("Rightist Parties ",  "SES Q", (i-5)) 
      if(i %in% c(10)) ses <- paste0("Rightist Parties ",  "All SES")
      
      gi <- ggiplot(Models[[i]]) 
      gp <- ggplot(gi$data, aes(x, estimate)) +
        geom_point() + 
        geom_hline(yintercept=0, linetype="longdash") +
        geom_errorbar(aes(ymin=ci_low,ymax=ci_high), width=0.4, size=.6) +
        geom_line(linetype="dotted") +
        scale_x_continuous(breaks = valar,  labels = valar, limits = c(1981, 2023)) +
        scale_y_continuous(limits = c(-.3, .9), breaks = round(seq(-.3, .9, by = .1), digits=1)) +
        xlab("Election Year") +
        ylab("Effect on Candidacy") +
        ggtitle(ses) +
        theme_classic(base_size = 14) +
        theme(legend.title=element_blank()) +
        theme(legend.position = "bottom", panel.grid.major.y = element_line(color = "grey95"))
      
      gp
      #ggsave(paste0("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Figures/Fig_", j, "_", i, "_hog.pdf"), width = 10, height =7.5, units = "in") 
      
      assign(paste0("gp", i), gp)
    }  

  
  modelsummary(Models[c(5, 4, 10, 9)], output = "C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Tables/TableFigA18.tex")
  ggarrange(gp5, gp4, gp10, gp9)
  ggsave("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Figures/FigureA18.pdf", width = 15, height =10, units = "in") 
  
sink()  
  
  
  